From Open Data to Open Analyses—New Opportunities for Environmental Applications?

Klein, Thomas; Nilsson, Mats; Persson, Anders; Håkansson, Bertil

doi:10.3390/environments4020032

Cited by 28 publications

(20 citation statements)

References 12 publications

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“…GEE combines a large data archive of satellite imagery with a computational platform. The platform enables scientists to conduct research on environmental issues on a variety of spatial and temporal scales [41][42][43][44]). For this study, we used the USGS Landsat 8 surface reflectance product.…”

Section: Methods Overviewmentioning

confidence: 99%

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

118

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Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.

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Section: Methods Overviewmentioning

confidence: 99%

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

118

View full text Add to dashboard Cite

show abstract

“…The capabilities of GEE as a platform which can deliver at a planetary scale are detailed in Gorelick, et al [28]. Various studies have been carried out using the GEE at a variety of scales for different purposes (see e.g., [29][30][31]…”

Section: Computational Platformmentioning

confidence: 99%

“…Various studies have been carried out using the GEE 128 at a variety of scales for different purposes (see e.g. [29], [30] and [31]).…”

Section: Computational Platformmentioning

confidence: 99%

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

Poortinga¹,

Clinton

Saah

et al. 2018

Remote Sensing

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In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web-based application by measuring improvement or degradation in vegetation based on the user-defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud-computing environment could be a cost-effective and useful tool for land managers and other practitioners

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“…In recent years, the increased availability of free-of-charge satellite data has allowed the study of many natural and human-made processes at low cost and has boosted research in many fields [1][2][3][4]. For instance, the Sentinel satellite constellation of the Copernicus program of the European Union [5] provides synthetic aperture radar (SAR) and multispectral data with global coverage, high-frequency pass, and high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Potential and Limitations of Open Satellite Data for Flood Mapping

et al. 2018

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Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

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From Open Data to Open Analyses—New Opportunities for Environmental Applications?

Cited by 28 publications

References 12 publications

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

Potential and Limitations of Open Satellite Data for Flood Mapping

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